Predicting the impact of non-coding variants on DNA methylation.

Abstract:

:DNA methylation plays a crucial role in the establishment of tissue-specific gene expression and the regulation of key biological processes. However, our present inability to predict the effect of genome sequence variation on DNA methylation precludes a comprehensive assessment of the consequences of non-coding variation. We introduce CpGenie, a sequence-based framework that learns a regulatory code of DNA methylation using a deep convolutional neural network and uses this network to predict the impact of sequence variation on proximal CpG site DNA methylation. CpGenie produces allele-specific DNA methylation prediction with single-nucleotide sensitivity that enables accurate prediction of methylation quantitative trait loci (meQTL). We demonstrate that CpGenie prioritizes validated GWAS SNPs, and contributes to the prediction of functional non-coding variants, including expression quantitative trait loci (eQTL) and disease-associated mutations. CpGenie is publicly available to assist in identifying and interpreting regulatory non-coding variants.

journal_name

Nucleic Acids Res

journal_title

Nucleic acids research

authors

Zeng H,Gifford DK

doi

10.1093/nar/gkx177

subject

Has Abstract

pub_date

2017-06-20 00:00:00

pages

e99

issue

11

eissn

0305-1048

issn

1362-4962

pii

3072752

journal_volume

45

pub_type

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